The present invention relates to methods and apparatus for image classification. In particular, it relates to segmentation and classification of medical MRI images based on fat and/or water content.
MRI systems are known. FIG. 1 shows a diagram of a magnetic resonance imaging (MRI) system 100 enabled to perform the methods that are an aspect of the present invention. The MRI system 100 includes a magnet system 112, a control system 120, a gradient coil driver 130, and an RF coil driver 128. The magnet system 112 includes a magnet 114, a gradient coil 116, and an RF coil 118. The control system 120 includes a sequence control unit 122, a data acquisition unit 126, and a controller 124 that controls the operations of the sequence control unit 22 and the data acquisition unit 126. Control system 120 may be implemented via any type of processing device(s), such as on a single computing device or as multiple computing devices networked together (e.g., over a LAN). The control system 120 may provide output signals to at least one display device 140, which may be a computer screen to display an image that is generated in accordance with one or more aspects of the present invention. A display device may also be an apparatus that generates a film containing an image. The control system 120 may also have an input device 141, which may provide control commands to the controller. A control device may be a keyboard, it may also be a mouse or a track-ball or any other device that can provide commands to the controller. The system may also have a plurality of input devices.
The magnet 114 includes resistance or superconducting coils (not shown) that generate a steady, uniform magnetic field. The uniform magnetic field is generated in a scanning space or region in which the subject to be examined is disposed. For example, if the subject is a person or patient to be examined, the person or portion of the person to be examined is disposed in the scanning region.
The gradient coil 116 generates magnetic field gradients that are used to cause the strength of the static magnetic field produced by the magnet 114 to undergo gradients in the x, y, and z directions or combinations thereof. The gradient coil driver 130 is in communication with the gradient coil 116 and applies a driving signal to the gradient coil 116 for the purpose of generating magnetic field gradients.
The RF coil driver 128 is in communication with the RF coil 118 and transmits a driving signal to the RF coil 118. In response to receiving the driving signal, the RF coil 118 produces RF excitation signals (referred to as “RF pulses”), which are used for generating species (e.g., exciting nuclei) in the region of interest (e.g., an organ) of the subject being imaged within the space of the static magnetic field. The species generate a resonance signal that is detected by the RF coil 118. In some embodiments, a separate coil is used to detect the resonance signal. The data acquisition unit 126, which is in communication with the RF coil 118, acquires the resonance signal (sometimes referred to as an “echo”) from the RF coil 118. The resonance signal is defined in a two-dimensional frequency domain or Fourier space, referred to as “k-space”. The data acquisition unit 126 samples and digitizes the resonance signal and provides the resulting signal to the controller 124 as digital data for storage and/or further processing.
The controller 124 processes the digital data to obtain an image of the region of interest. The controller 124 may apply a variety of known image processing techniques to construct the image, which may be viewed on a display 140 coupled to the controller 124. The display may be provided, for example, as a monitor or a terminal, such as a CRT or flat panel display. It may also be a device that creates an image on film.
The sequence control unit 122 is connected to each of the gradient coil driver 130, the RF coil driver 128, and the controller 124. The controller 124 has a memory that stores programs having instructions that cause the sequence control unit 116 to direct the delivery of RF pulses and gradient fields from the RF coil 118 and the gradient coil 116 to the region of interest. In response to receiving control signals provided from the sequence control unit 122, the gradient coil driver 130 provides a driving signal to the gradient coil 116, and the RF coil driver 128 provides a driving signal to the RF coil 118. These and other details on an MRI scanner may be found in U.S. Pat. No. 7,292,039 to Laub et al. issued on Nov. 6, 2007 which is incorporated herein by reference.
It is to be understood that the actual implementation of components of an MRI system may be realized in different embodiments, for instance as demonstrated in the different embodiments that are available in the Siemens MAGNETOM series of MRI systems and that the diagram as provided herein is not intended to be limiting to a single embodiment of an MRI system.
Dixon introduced a new image technique to create MRI images of water only and fat only in “Dixon W. T. Simple proton spectroscopic imaging. Radiology 1984; 153:189-194” which is incorporated herein by reference in its entirety.
The technique as disclosed by Dixon is based on periodic variations in the free induction decay signal (FID) in acquiring an image. By applying different echo times one can acquire a “sum of fat and water” image and a “difference of fat and water” image, from which one may determine a fat image and a water image. One may call the original Dixon method a two-point method. The reconstructed images in the two-point method may suffer from errors due to inhomogeneities in the intensity of the magnetic field. Glover et al. in “Glover G H, Schneider E. Three-point Dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magn Reson Med 1991; 18:371-383” addressed the issue by acquiring three images in what is known as the 3-point Dixon method, which is incorporated herein by reference in its entirety.
Single-point Dixon methods are also known, for instance as disclosed in “Jong Bum Son et al, Concepts in Magnetic Resonance Part B: Magnetic Resonance Engineering, Volume 33B Issue 3, Pages 152-162 Published Online: 10 Jul. 2008, Wiley Publications, Single-point Dixon water-fat imaging using 64-channel single-echo acquisition MRI.” and “A single-point Dixon technique for fat-suppressed fast 3D gradient-echo imaging with a flexible echo time”, Jingfei Ma. Journal of Magnetic Resonance Imaging, February 2008, which are both incorporated herein by reference in their entirety.
These Dixon methods are capable of computing pure fat and pure water images from MR raw data making use of the defined difference in precession times between bound water protons and bound fat protons.
The One-point Dixon method is thus one embodiment of an imaging method. The methods and systems of distinguishing and classifying images as a fat image or a predominantly fat image or as a water image, or a predominantly water image as one or more aspects of the present invention can be applied to different MRI acquisition techniques, such as 1-point, 2-point, 3-point and any multi-point Dixon techniques, as long as a combined fat/water image, and at least one of a separate fat image or a predominantly fat image and a separate water image or predominantly water image are generated.
Although the separation from image data into two different images of mainly separate but connected components x and y such as water and fat by the Dixon methods is known, it is not straightforward to conclude that x is fat and y is water. This is especially the case for automatic recognition of an image as a fat image or as a water image by a machine such as a processor. While it is possible for a human operator to conclude that an image is a fat image, based for instance on a knowledge of a context, it is difficult to make such a conclusion automatically by a machine or by an untrained operator. Common heuristics often fail.
The MR scanning process acquires complex data containing phase and magnitude information. Dixon methods are based on relative phase information based on the assumption of defined phase differences at defined echo times caused by the different precession times between protons bound to fat and protons bound to water. Due to its periodic nature and the superimposition of other effects, phase information is not absolute which makes it difficult to determine automatically by a machine that a generated MRI image is a fat image or a water image.
The Dixon methods are capable of separating components which are connected by neighboring voxels. If there is no connection, then one component might be classified correctly and the other incorrectly. One may, for instance, take an MRI image of a head of a person with his arms next to the head. Because the pixels or voxels in an arm are not connected with the ones of the head in an MRI image, one may correctly assign the labels fat and water in the head image, but incorrectly in the arm image.
Accordingly, novel and improved apparatus and methods are required to classify a Dixon method based image automatically in a robust way into its appropriate class.